# TODO：导入必要的库和模块
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
import pickle
from tqdm import tqdm

# TODO：加载数字数据集
digits = load_digits()
X = digits.data
y = digits.target

# TODO：将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# TODO：初始化变量以存储最佳准确率，相应的k值和最佳knn模型
best_accuracy = 0
best_k = 0
best_knn_model = None

# TODO：初始化一个列表以存储每个k值的准确率
accuracy_scores = []

# TODO：尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
k_values = range(1, 41)

for k in tqdm(k_values, desc="Testing K values"):
    # 创建KNN分类器
    knn = KNeighborsClassifier(n_neighbors=k)
    
    # 训练模型
    knn.fit(X_train, y_train)
    
    # 预测
    y_pred = knn.predict(X_test)
    
    # 计算准确率
    accuracy = accuracy_score(y_test, y_pred)
    accuracy_scores.append(accuracy)
    
    # 更新最佳模型
    if accuracy > best_accuracy:
        best_accuracy = accuracy
        best_k = k
        best_knn_model = knn

# TODO：将最佳KNN模型保存到二进制文件
with open('best_knn_model.pkl', 'wb') as f:
    pickle.dump(best_knn_model, f)

# TODO：打印最佳准确率和相应的k值
print(f"Best k: {best_k}, Best accuracy: {best_accuracy:.4f}")

# 绘制准确率图表并保存为PDF
plt.figure(figsize=(10, 6))
plt.plot(k_values, accuracy_scores, marker='o', linestyle='-')
plt.axvline(x=best_k, color='red', linestyle='--', label=f'Best k={best_k}')
plt.text(best_k, best_accuracy, f'k={best_k}, Acc={best_accuracy:.3f}', 
         fontsize=10, ha='right', va='bottom')

plt.xlabel('k value')
plt.ylabel('Accuracy')
plt.title('Accuracy of different k values')
plt.grid(True, alpha=0.3)
plt.legend()

# 保存准确率图表
plt.savefig('accuracy_plot.pdf', format='pdf', bbox_inches='tight')
plt.close()